Development and application of an automatic image classification system for the mesozooplankton in the East China Sea
Date Issued
2009
Date
2009
Author(s)
Chang, Chun-Yi
Abstract
We developed an automatic classification system for mesozooplankton in the East China Sea, a region with complicated environmental variations ranging from coastal areas affected by river runoff to the shelf break influenced by the Kuroshio. Considering the large variation of water masses, we test the hypothesis that a water mass-specific training set (WTS) would perform better than a regional training set (RTS, which is randomly assembled from all stations). To test the water mass specificity, we evaluated pair-wise cross predictions for all stations. We found that cross-prediction accuracy decreased with an increase in environmental dissimilarity; for example, mutual predictions between coastal stations performed better than those using coastal stations to predict Kuroshio stations. Furthermore, the cross-prediction matrix is significantly correlated with the similarity matrix derived from environmental variables. These results suggest clear water mass specificity in training sets. However surprisingly, the prediction accuracy (with an average of 75%) of RTS performs equally well as the best WTS results for each station. This is mainly due to the contribution of dominant zooplankton categories to the overall accuracy rate. This suggests the benefit of WTS is limited for the coarse taxonomic resolution (order or higher) employed here. The machine-identified species composition (albeit containing ~38.5% error) still explained a significant amount of variance associated with the environmental gradient, demonstrating the potential capability of the automatic classification system.
Subjects
water mass-specific training set
regional training set
image analysis
machine-learning
hydrography
zooplankton community
Type
thesis
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